ReviewFrontiers in medicine2024
Advances in the clinical application of machine learning in acute pancreatitis: a review.
Review in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Role of Oxidative Stress and Clinical Implications in Acute Pancreatitis.World journal of surgery · 2026Pooled it
- Pooled it
- Review
- Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Acute pancreatitis: mechanisms and therapeutic approaches.Signal transduction and targeted therapy · 2026Review
- Post-translational modification crosstalk in acute pancreatitis: linking acinar stress to innate immune amplification and tissue repair.Frontiers in immunology · 2026Review
- Predicting the acute pancreatitis severity with multi-machine learning models: constructing an online prediction platform.Frontiers in cellular and infection microbiology · 2026Article
- Evaluating the national early warning score (NEWS) and NEWS-calcium for predicting adverse outcomes in acute pancreatitis: a prospective cohort study in Vietnam.BMC gastroenterology · 2025Article
- Decoding dynamic lipase trajectory patterns and in-hospital mortality in acute pancreatitis: insights from machine learning in intensive care units.European journal of medical research · 2025Article
- Integrating Feature Selection, Machine Learning, and SHAP Explainability to Predict Severe Acute Pancreatitis.Diagnostics (Basel, Switzerland) · 2025Article
- Evaluating the Role of Artificial Intelligence in Making Clinical Decisions for Treating Acute Pancreatitis.Journal of clinical medicine · 2025Article
- AI and Machine Learning for Precision Medicine in Acute Pancreatitis: A Narrative Review.Medicina (Kaunas, Lithuania) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional disease prediction models and scoring systems for acute pancreatitis (AP) are often inadequate in providing concise, reliable, and effective predictions regarding disease progression and prognosis. As a novel interdisciplinary field within artificial intelligence (AI), machine learning (ML) is increasingly being applied to various aspects of AP, including severity assessment, complications, recurrence rates, organ dysfunction, and the timing of surgical intervention. This review focuses on recent advancements in the application of ML models in the context of AP.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.